What is class imbalance and how do you deal with it?

Class imbalance occurs when one class has significantly more data points than another in a dataset, causing models to favor the majority class; you can deal with it using resampling (oversampling the minority, undersampling the majority, or both), synthetic data generation (like SMOTE), cost-sensitive learning (weighted loss functions), or ensemble methods, focusing on metrics like Precision-Recall rather than just accuracy.

How to deal with class imbalance?

How to solve class imbalance problem? Resampling: You can oversample the minority class or undersample the majority class to balance the dataset. Synthetic Data: Generate new samples for the minority class using techniques like SMOTE (Synthetic Minority Over-sampling Technique).

What technique combats class imbalance?

Ensemble techniques such as Bagging, Boosting (e.g., AdaBoost), and Stacking can improve model performance on imbalanced data. Combining multiple models or assigning higher weights to the minority class during ensemble learning can enhance the model's ability to capture minority class patterns.

What does class imbalance mean?

Class imbalance (CI) in classification problems arises when the number of observations belonging to one class is lower than the other. Ensemble learning combines multiple models to obtain a robust model and has been prominently used with data augmentation methods to address class imbalance problems.

How to overcome imbalance?

Now, let's dive into some of the most effective techniques for overcoming data imbalance.

1. SMOTE (Synthetic Minority Over-sampling Technique) ...
2. Cost-Sensitive Learning. ...
3. Under-sampling the Majority Class. ...
4. Ensemble Methods.

Handling Imbalanced Dataset in Machine Learning: Easy Explanation for Data Science Interviews

How to improve the classification accuracy of class imbalanced data?

1. Random under-sampling. Random under-sampling is a simple technique to handle class imbalance (or imbalanced data). ...
2. Random over-sampling. ...
3. Synthetic over-sampling: SMOTE. ...
4. Choose the algorithm wisely. ...
5. Play with the loss function. ...
6. Solve an anomaly detection problem.

What is the class imbalance ratio?

The 'Imbalance Ratio' is defined as the number of negative class data instances divided by the number of positive classes in binary datasets. It helps categorize datasets based on the imbalance between the minority and majority classes.

What are the approaches to class imbalance?

The common approach to class imbalance is resampling. These can entail oversampling the majority class, undersampling the minority class, or a combination of both. In this post, I use vivid visuals and code to illustrate these strategies for class imbalance: Random oversampling.

How do you describe imbalance?

noun. the state or condition of lacking balance, as in proportion or distribution. faulty muscular or glandular coordination.

What are the 4 types of ML?

There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforcement.

What is an example of a classification problem?

A common example of classification comes with detecting spam emails. To write a program to filter out spam emails, a computer programmer can train a machine learning algorithm with a set of spam-like emails labelled as spam and regular emails labelled as not-spam.

Which technique is commonly used for handling imbalanced classes in classification problems?

Resampling (Oversampling and Undersampling)

When we are using an imbalanced dataset, we can oversample the minority class using replacement. This technique is called oversampling. Similarly, we can randomly delete rows from the majority class to match them with the minority class which is called undersampling.

Which algorithm is best for imbalanced data?

Tree-based algorithms often perform well on imbalanced datasets. Boosting algorithms ( e.g AdaBoost, XGBoost,…) are ideal for imbalanced datasets because higher weight is given to the minority class at each successive iteration. during each interation in training the weights of misclassified classes are adjusted.

What are the challenges with imbalanced class?

Challenges and Limitations

Imbalanced classes can lead to misleading performance measures, as classifiers are prone to bias towards the majority class, thereby overlooking the minority class which may be of greater interest. This can lead to overgeneralization or underfitting issues.

Is 60/40 imbalanced data?

An imbalance in the data is usually considered an issue when the distribution of classes is skewed more than 60-40% ratio.

What is the algorithm level approach?

Unlike the data-level approach, algorithm-level is a dedicated algorithm that directly learns the imbalanced distribution from different datasets based on cost-sensitive methods and ensemble learning [20] .

What is the root cause of imbalance?

Balance disorders can be caused by certain health conditions, medications, or a problem in the inner ear or the brain. A balance disorder can profoundly affect daily activities and cause psychological and emotional hardship.

What is imbalance a symptom of?

Imbalance, or feeling unsteady/dizzy, is a symptom of problems in your inner ear (vestibular system), brain, muscles, joints, or vision, caused by things like infections, head injuries, neurological conditions (Parkinson's, MS), circulation issues (low BP), medications, or simple dehydration, often indicating issues with coordination or sensory input. 

What is an example of a class imbalance?

In a class-imbalanced dataset, one label is considerably more common than the other. In the real world, class-imbalanced datasets are far more common than class-balanced datasets. For example, in a dataset of credit card transactions, fraudulent purchases might make up less than 0.1% of the examples.

What does imbalance mean?

An imbalance means a lack of balance, proportion, or equilibrium between two or more things, creating an uneven or disproportionate state. It describes situations where elements aren't equally distributed, such as a chemical imbalance in the body, a trade imbalance in economics, or an imbalanced relationship where one person invests more. 

What are the effects of class imbalance?

Class imbalance is a condition where the amount of data in the minority class is smaller than that of the majority class. The impact of the class imbalance in the dataset is the occurrence of minority class misclassification, so it can affect classification performance.

How does class imbalance affect decision trees?

When training a decision tree on imbalanced data, you may notice: The model predicts the majority class in most leaves. Overall accuracy looks high, but minority class recall/precision is very poor. The tree may not create meaningful splits for minority class data at all.

What techniques can be used to handle a class imbalance in a dataset?

To handle a class imbalance in datasets, one can use techniques such as Synthetic Minority Over-sampling Technique (SMOTE), Random Undersampling, and proper model evaluation with Cross-Validation. Stratified random sampling is also relevant as it ensures a representative distribution of classes in sampling.

How to handle class imbalance in object detection?

Hard sampling is a commonly used method for addressing class imbalance in object detection. By choosing a subset of the available set of labeled bounding boxes, it corrects the imbalance. Heuristic approaches are used to make the selection. Such approaches will ignore the non-selected objects.